Wireless networks rely on a large number of individual base stations or cells to provide high capacity wireless services over large coverage areas such as market areas (e.g. cities), surrounding residential areas (e.g. suburbs, counties), highway corridors and rural areas. Maximizing the capacity of such networks while utilizing limited licensed radio frequency spectrum involves reusing time and frequency channel resources throughout the cells in the network. Third and fourth generation commercial wireless network technologies (3G and 4G) maximize system performance via high levels of time and frequency channel resources whereby most, if not all, cells in a network are provisioned to utilize the same radio frequency spectrum. This can lead to excessively high levels of interference and poor performance, particularly for users operating at the overlapping boundary regions between neighboring cells. Advanced signal processing and resource scheduling techniques in the time, frequency, and/or code domains are typically used to manage the co-channel interference that results from high levels of radio frequency spectrum reuse, but conventional methods force network planners and optimization teams to rely on conservative reuse planning approaches to avoid excessive co-channel interference between neighboring cells.
Self Optimizing Network (SON) technologies such as coordinated multi-node scheduling introduce dynamic methods of coordinating radio frequency resource utilization between nearby cells in order to improve overall resource utilization efficiency while simultaneously avoiding excessive co-channel interference between neighboring cells. These multi-cell SON automation technologies require an identification of nearby cells within the wireless network that have a high probability of causing interference to one another, and thus become candidates for multi-cell resource coordination. Advanced network performance monitoring technologies benefit from the identification of ‘clusters’ of nearby cells that have strong interdependencies from a radio frequency resource sharing standpoint (that is, clusters of cells that are highly likely to interfere with each other without sufficient coordination of resources).
Traditionally, the identification of close proximity nearby neighboring cells has been based on preplanned estimates utilizing geographic positioning and predictions of radio frequency (RF) path loss isolation or coupling between network cells to create a neighbor cell list. This approach relies on many assumptions regarding real world propagation conditions and does not dynamically respond to changing network conditions such as the addition or removal or re-engineering of network cells (e.g. cell splitting), nor does it respond to seasonal changes in radio frequency propagation conditions (e.g. seasonal foliage changes) or actual cell utilization patterns within the network.
The primary purpose of neighbor cell lists is to identify a limited set of cells, and transmit the list to user equipment for use in a handoff operation. As such, it is not necessary to analyze variables beyond geographic proximity, and possibly limited deductions on RF characteristics, in order to establish the list. Neighbor cell lists are typically created when a base station is initially installed, and may be periodically updated as new cells are established within a limited geographical area.
Neighbor cell lists have limited utility beyond handover operations. Neighbor lists don't account for current conditions, actual use patterns, or other dynamic variables. Due to size limitations, neighbor lists do not include all cells that affect, or could potentially affect, transmission characteristics of a reference cell. Certain optimization and maintenance operations can benefit from additional information relevant to a particular reference cell without placing a large burden on network equipment.